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Widespread inference of weighted microRNA-mediated gene regulation in cancer transcriptome analysis

MicroRNAs (miRNAs) comprise a gene-regulatory network through sequence complementarity with target mRNAs. Previous studies have shown that mammalian miRNAs decrease many target mRNA levels and reduce protein production predominantly by target mRNA destabilization. However, it has not yet been fully...

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Autores principales: Suzuki, Hiroshi I., Mihira, Hajime, Watabe, Tetsuro, Sugimoto, Koichi, Miyazono, Kohei
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Oxford University Press 2013
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3597654/
https://www.ncbi.nlm.nih.gov/pubmed/23275554
http://dx.doi.org/10.1093/nar/gks1439
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author Suzuki, Hiroshi I.
Mihira, Hajime
Watabe, Tetsuro
Sugimoto, Koichi
Miyazono, Kohei
author_facet Suzuki, Hiroshi I.
Mihira, Hajime
Watabe, Tetsuro
Sugimoto, Koichi
Miyazono, Kohei
author_sort Suzuki, Hiroshi I.
collection PubMed
description MicroRNAs (miRNAs) comprise a gene-regulatory network through sequence complementarity with target mRNAs. Previous studies have shown that mammalian miRNAs decrease many target mRNA levels and reduce protein production predominantly by target mRNA destabilization. However, it has not yet been fully assessed whether this scheme is widely applicable to more realistic conditions with multiple miRNA fluctuations. By combining two analytical frameworks for detecting the enrichment of gene sets, Gene Set Enrichment Analysis (GSEA) and Functional Assignment of miRNAs via Enrichment (FAME), we developed GSEA–FAME analysis (GFA), which enables the prediction of miRNA activities from mRNA expression data using rank-based enrichment analysis and weighted evaluation of miRNA–mRNA interactions. This cooperative approach delineated a better widespread correlation between miRNA expression levels and predicted miRNA activities in cancer transcriptomes, thereby providing proof-of-concept of the mRNA-destabilization scenario. In an integrative analysis of The Cancer Genome Atlas (TCGA) multidimensional data including profiles of both mRNA and miRNA, we also showed that GFA-based inference of miRNA activity could be used for the selection of prognostic miRNAs in the development of cancer survival prediction models. This approach proposes a next-generation strategy for the interpretation of miRNA function and identification of target miRNAs as biomarkers and therapeutic targets.
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spelling pubmed-35976542013-03-15 Widespread inference of weighted microRNA-mediated gene regulation in cancer transcriptome analysis Suzuki, Hiroshi I. Mihira, Hajime Watabe, Tetsuro Sugimoto, Koichi Miyazono, Kohei Nucleic Acids Res Methods Online MicroRNAs (miRNAs) comprise a gene-regulatory network through sequence complementarity with target mRNAs. Previous studies have shown that mammalian miRNAs decrease many target mRNA levels and reduce protein production predominantly by target mRNA destabilization. However, it has not yet been fully assessed whether this scheme is widely applicable to more realistic conditions with multiple miRNA fluctuations. By combining two analytical frameworks for detecting the enrichment of gene sets, Gene Set Enrichment Analysis (GSEA) and Functional Assignment of miRNAs via Enrichment (FAME), we developed GSEA–FAME analysis (GFA), which enables the prediction of miRNA activities from mRNA expression data using rank-based enrichment analysis and weighted evaluation of miRNA–mRNA interactions. This cooperative approach delineated a better widespread correlation between miRNA expression levels and predicted miRNA activities in cancer transcriptomes, thereby providing proof-of-concept of the mRNA-destabilization scenario. In an integrative analysis of The Cancer Genome Atlas (TCGA) multidimensional data including profiles of both mRNA and miRNA, we also showed that GFA-based inference of miRNA activity could be used for the selection of prognostic miRNAs in the development of cancer survival prediction models. This approach proposes a next-generation strategy for the interpretation of miRNA function and identification of target miRNAs as biomarkers and therapeutic targets. Oxford University Press 2013-03 2012-12-26 /pmc/articles/PMC3597654/ /pubmed/23275554 http://dx.doi.org/10.1093/nar/gks1439 Text en © The Author(s) 2012. Published by Oxford University Press. http://creativecommons.org/licenses/by-nc/3.0/ This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/3.0/), which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.
spellingShingle Methods Online
Suzuki, Hiroshi I.
Mihira, Hajime
Watabe, Tetsuro
Sugimoto, Koichi
Miyazono, Kohei
Widespread inference of weighted microRNA-mediated gene regulation in cancer transcriptome analysis
title Widespread inference of weighted microRNA-mediated gene regulation in cancer transcriptome analysis
title_full Widespread inference of weighted microRNA-mediated gene regulation in cancer transcriptome analysis
title_fullStr Widespread inference of weighted microRNA-mediated gene regulation in cancer transcriptome analysis
title_full_unstemmed Widespread inference of weighted microRNA-mediated gene regulation in cancer transcriptome analysis
title_short Widespread inference of weighted microRNA-mediated gene regulation in cancer transcriptome analysis
title_sort widespread inference of weighted microrna-mediated gene regulation in cancer transcriptome analysis
topic Methods Online
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3597654/
https://www.ncbi.nlm.nih.gov/pubmed/23275554
http://dx.doi.org/10.1093/nar/gks1439
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